Founding AI Engineer
About the role
Seeking an experienced AI/ML founding engineer to build AI solutions on an observability platform for automatic issue detection and resolution.
Responsibilities
- Build AI-Powered Remediation Systems: Design and implement machine learning models to identify, diagnose, and resolve system issues.
- Own the AI/ML Pipeline: Manage data collection, preprocessing, model training, evaluation, and deployment.
- Integrate with Observability Stack: Collaborate with platform team to embed AI into logs, metrics, traces.
- Experiment and Iterate: Prototype and test different models like anomaly detection, root cause analysis, LLM insights.
- Collaborate Cross-Functionally: Work with product, backend, and DevOps teams to align AI capabilities.
- Set the Technical Direction: Contribute to architecture decisions and AI/ML best practices.
- Ensure Reliability and Scalability: Build systems that perform reliably at scale in production.
- Stay Ahead of the Curve: Keep up with latest AI/ML and observability advancements.
Qualifications
- Engineers with experience building AI products.
- Solid software engineering skills in Python and TypeScript.
- Understanding of observability tools like Prometheus, OpenTelemetry.
- Owner mindset: comfortable in fast-paced, ambiguous environments with high ownership.
Skills

About Middleware
AI-based full stack observability platform
Middleware is a full-stack cloud observability platform. We allow the dev and ops teams to debug the issue faster by bringing all the metrics, logs, traces, and events to a single unified timeline and train data to AI so it can give better insight into infrastructure and application. - Single command installation to collect all the metrics, logs, traces, and events. - AI-based anomaly and error detection - GPT 4-based error resolution - Support Infrastructure monitoring, Logs, APM, Kubernetes Monitoring, Synthetic Monitoring, RUM, Custom dashboard, and Alerts. - Full-stack observability, observe any stack at any scale. We are at Middleware and wanted to make cloud observability faster, cheaper, and better.
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